Quantitative diagnostic methods using multiple parameters

ABSTRACT

Materials and Methods related to diagnosing a clinical condition in a subject, or determining the subject&#39;s predisposition to develop the clinical condition, using a multi-parameter system to measure a plurality of parameters and an algorithm to determine a disease score.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.11/850,550, filed on Sep. 5, 2007, which claims benefit of U.S.Provisional Application No. 60/824,471, filed on Sep. 5, 2006, and U.S.Provisional Application No. 60/910,217, filed on Apr. 5, 2007, both ofwhich are incorporated herein by reference in their entirety.

TECHNICAL FIELD

This document relates to materials and methods for diagnosing orassessing a clinical condition in a subject, or determining a subject'spredisposition to develop a clinical condition, using algorithms todetermine a disease score based a combination of weighted parameters.

BACKGROUND

Most clinical disorders do not arise due to a single biological change,but rather are the result of an interaction of multiple factors. Thus,different individuals affected by the same clinical condition maypresent with a different range of symptoms or a different extent ofsymptoms, depending on the specific changes within each individual. Theability to determine disease status on an individual basis thus would beuseful for accurate assessment of a subject's specific status. There isa need, however, for reliable methods for diagnosing or determiningpredisposition to clinical conditions, or for assessing a subject'sdisease status or response to treatment.

SUMMARY

This document is based in part on the identification of methods forestablishing diagnosis, prognosis, or predisposition to particularclinical conditions. The methods can include developing an algorithmthat includes multiple parameters such as biomarkers, measuring themultiple parameters, and using the algorithm to determine a quantitativediagnostic score. In some embodiments, algorithms for application ofmultiple biomarkers from biological samples such as serum or plasma canbe developed for patient stratification and identification ofpharmacodynamic markers.

The approach described herein can differ from some of the moretraditional approaches to biomarkers in the construction of an algorithmversus analyzing single markers or groups of single markers. Algorithmscan be used to derive a single value that reflects disease status,prognosis, or response to treatment. As described herein, highlymultiplexed microarray-based immunological tools can be used tosimultaneously measure of multiple parameters. An advantage of usingsuch tools is that all results can be derived from the same sample andrun under the same conditions at the same time. High-level patternrecognition approaches can be applied, and a number of tools areavailable, including clustering approaches such as hierarchicalclustering, self-organizing maps, and supervised classificationalgorithms (e.g., support vector machines, k-nearest neighbors, andneural networks). The latter group of analytical approaches is likely tobe of substantial clinical use.

In one aspect, this document features a process for diagnosingdepression in a subject, comprising (a) providing numerical values for aplurality of parameters predetermined to be relevant to depression; (b)individually weighting each of the numerical values by a predeterminedfunction, each function being specific to each parameter; (c)determining the sum of the weighted values; (d) determining thedifference between the sum and a control value; and (e) if thedifference is greater than a predetermined threshold, classifying theindividual as having depression, or, if the difference is not differentthan the predetermined threshold, classifying the individual as nothaving depression. The depression can be associated with majordepressive disorder.

The parameters can be selected from the group consisting ofbrain-derived neurotrophic factor (BDNF), interleukin- (IL-) 7, IL-10,IL-13, IL-15, IL-18, fatty acid binding protein (FABP), alpha-1antitrypsin (A1AT), beta-2 macroglobulin (B2M), factor VII, epithelialgrowth factor (EGF), alpha-2-macroglobulin (A2M), glutathioneS-transferase (GST), RANTES, tissue inhibitor of matrixmetalloproteinase-1 (TIMP-1), plasminogen activator inhibitor-1 (PAI-1),thyroxine, and cortisol. The parameters can be selected from the groupconsisting of BDNF, A2M, IL-10, IL-13, IL-18, cortisol, and thyroxine(e.g., BDNF, A2M, IL-10, and IL-13; BDNF, A2M, IL-10, and IL-18; BDNF,A2M, IL-13, and IL-18; BDNF, A2M, and IL-10; BDNF, A2M, and IL-13; orBDNF, A2M, and IL-18). The numerical values can be biomarker levels in abiological sample from the subject. The biological sample can be wholeblood, serum, plasma, urine, or cerebrospinal fluid. The subject can bea human. The predetermined threshold can be statistical significance(e.g., p<0.05). Methods for determining statistical significance caninclude those routinely used in the art, for example.

In another aspect, this document features a process for diagnosingdepression in a subject, comprising (a) providing numerical values for aplurality of parameters predetermined to be relevant to depression,individually weighting each of the numerical values by a predeterminedfunction, each function being specific to each parameter, determiningthe sum of the weighted values, determining the difference between thesum and a control value; and (b) if the difference is greater than apredetermined threshold, classifying the individual as havingdepression, or, if the difference is not greater than the predeterminedthreshold, classifying the individual as not having depression.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention pertains. Although methods and materialssimilar or equivalent to those described herein can be used to practicethe invention, suitable methods and materials are described below. Allpublications, patent applications, patents, and other referencesmentioned herein are incorporated by reference in their entirety. Incase of conflict, the present specification, including definitions, willcontrol. In addition, the materials, methods, and examples areillustrative only and not intended to be limiting.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a flow diagram outlining the selection of biomarkers.

FIG. 2 is a flow diagram for the development of a disease specificlibrary or panel with an algorithm for diagnostic development.

FIG. 3 is a flow diagram for development of a diagnostic score.

FIG. 4 is a pair of illustrations depicting the binding of an antigen toits specific antibody on a substrate, resulting in an increase in theoptical thickness of the interference film when a light beam is directedat the substrate (left panel). The right panel depicts a substratehaving three discrete spots of antibodies, with analytes boundspecifically thereto (top panel).

FIG. 5 is a two-dimensional view of anti-interleukin-1 beta (anti-IL-1beta) printed nanostructured chip (16 spots are shown).

FIG. 6 is a three-dimensional view of the front portion of the same chipshown in FIG. 5.

FIG. 7 a three-dimensional view of the IL-1 beta antibody reacted withIL-1 beta standard. The graph in the right panel indicates the changesin peak height before (upper peaks) and after (lower peaks) reactionwith IL-1 beta.

FIG. 8 is a graph plotting diagnostic depression scores generated usinga multivariate analysis of sera from control (squares) and majordepressive disorder (MDD) (circles) individuals, which were analyzedusing a biomarker library panel of about 85 serum protein analytes.

FIG. 9 is a graph plotting brain-derived neurotrophic factor (BDNF)levels in the serum of normal male subjects and male MDD patients. Thedots represent measurements for each subject, with the embedded medianand the 5 and 95 percentile whiskers. The adjacent lines indicate themeans and the standard deviations from the mean.

FIG. 10 is a graph plotting serum interleukin (IL)-13 levels in normalmale subjects and male MDD patients. The dots represent measurements foreach subject, with the embedded median and the 5 and 95 percentilewhiskers. The adjacent lines indicate the means and the standarddeviations from the mean.

FIG. 11 is a graph plotting serum IL-18 levels in normal male subjectsand male MDD patients. At left for each group are the median and the 5and 95 percentile whiskers. The adjacent lines indicate the means andthe standard deviations from the mean.

FIG. 12 is a graph plotting serum fatty acid binding protein (FABP)levels in normal male subjects and male MDD patients. The dots representmeasurements for each subject, with the embedded median and the 5 and 95percentile whiskers. The adjacent lines indicate the means and thestandard deviations from the mean.

FIG. 13 is a graph plotting serum IL-10 levels in normal male subjectsand male MDD patients. At left for each group are the median and the 5and 95 percentile whiskers. The adjacent lines indicate the means andthe standard deviations from the mean.

FIG. 14 is a graph plotting serum IL-7 levels in normal male subjectsand male MDD patients. The dots represent measurements for each subject,with the embedded median and the 5 and 95 percentile whiskers. Theadjacent lines indicate the means and the standard deviations from themean.

FIG. 15 is a graph plotting serum glutathione S-transferase (GST) levelsin normal male subjects and male MDD patients. The dots representmeasurements for each subject, with the embedded median and the 5 and 95percentile whiskers. The adjacent lines indicate the means and thestandard deviations from the mean.

FIG. 16 is a graph plotting serum epidermal growth factor (EGF) levelsin normal male subjects and male MDD patients. The dots representmeasurements for each subject, with the embedded median and the 5 and 95percentile whiskers. The adjacent lines indicate the means and thestandard deviations from the mean.

FIG. 17 is a graph plotting serum plasminogen activator inhibitor-1(PAI-1) levels in normal male subjects and male MDD patients. The dotsrepresent measurements for each subject, with the embedded median andthe 5 and 95 percentile whiskers. The adjacent lines indicate the meansand the standard deviations from the mean.

FIG. 18 is a graph plotting serum RANTES levels in normal male subjectsand male MDD patients. The dots represent measurements for each subject,with the embedded median and the 5 and 95 percentile whiskers. Theadjacent lines indicate the means and the standard deviations from themean.

FIG. 19 is a graph plotting serum factor VII levels in normal malesubjects and male MDD patients. The dots represent measurements for eachsubject, with the embedded median and the 5 and 95 percentile whiskers.The adjacent lines indicate the means and the standard deviations fromthe mean.

FIG. 20 is a graph plotting serum IL-15 levels in normal male subjectsand male MDD patients. The dots represent measurements for each subject,with the embedded median and the 5 and 95 percentile whiskers. Theadjacent lines indicate the means and the standard deviations from themean.

FIG. 21 is a graph plotting serum tissue inhibitor of matrixmetalloproteinase-1 (TIMP-1) levels in normal male subjects and male MDDpatients. The dots represent measurements for each subject, with theembedded median and the 5 and 95 percentile whiskers. The adjacent linesindicate the means and the standard deviations from the mean.

FIG. 22 is a graph plotting serum tissue inhibitor of alpha-1antitrypsin (A1AT) levels in normal male subjects and male MDD patients.The dots represent measurements for each subject, with the embeddedmedian and the 5 and 95 percentile whiskers. The adjacent lines indicatethe means and the standard deviations from the mean.

FIG. 23 is a graph plotting serum tissue inhibitor of alpha-2macroglobulin (A2M) levels in normal male subjects and male MDDpatients. At left for each group are the median and the 5 and 95percentile whiskers. The adjacent lines indicate the means and thestandard deviations from the mean.

FIG. 24 is a graph plotting serum beta-2 macroglobulin (B2M) levels innormal male subjects and male MDD patients. The dots representmeasurements for each subject, with the embedded median and the 5 and 95percentile whiskers. The adjacent lines indicate the means and thestandard deviations from the mean.

FIG. 25 is a graph plotting serum cortisol levels in normal malesubjects and male MDD patients. The median and 5 and 95 percentilewhiskers are shown at the left of each data set, while the adjacentlines to the right indicate the mean and standard deviations from themean.

FIG. 26 is a graph plotting serum thyroxine levels in normal malesubjects and male MDD patients. The median and 5 and 95 percentilewhiskers are shown at the left of each data set, while the adjacentlines to the right indicate the mean and standard deviations from themean.

FIG. 27 is a graph plotting the results of partial least squaresdiscriminant analysis (PLS-DA) of sera from control (striped bars) andMDD (solid bars) patients, which were determined using a biomarkerlibrary panel of about 85 serum protein analytes to quantify 16 markers:BDNF, IL-7, IL-10, IL-13, IL-15, IL-18, FABP, adrenocorticotropichormone (ACTH), thyroxine, factor VII, EGF, A2M, GST, RANTES, TIMP-1,and PAI-1.

FIG. 28 is a flow diagram for a process wherein the side-effect profileof a chemical entity can be determined using a Molecular InteractionMeasurement System (MIMS, PHB Labs, Durham, N.C.).

DETAILED DESCRIPTION

This document is based in part on the identification of methods forestablishing a diagnosis, prognosis, or predisposition to particularclinical conditions by developing an algorithm, evaluating (e.g.,measuring) multiple parameters, and using the algorithm to determine aquantitative diagnostic score. Algorithms for application of multiplebiomarkers from biological samples such as serum or plasma can bedeveloped for patient stratification and identification ofpharmacodynamic markers. The approach described herein differs from someof the more traditional approaches to biomarkers in the construction ofan algorithm versus analyzing single markers or groups of singlemarkers.

Algorithms

Algorithms for determining diagnosis, prognosis, status, or response totreatment, for example, can be determined for any clinical condition.The algorithms used in the methods provided herein can be mathematicfunctions containing multiple parameters that can be quantified using,for example, medical devices, clinical evaluation scores, orbiological/chemical/physical tests of biological samples. Eachmathematic function can be a weight-adjusted expression of the levels ofparameters determined to be relevant to a selected clinical condition.The algorithms generally can be expressed in the format of Formula 1:

Diagnostic score=f(x1,x2,x3,x4,x5 . . . xn)  (1)

The diagnostic score is a value that is the diagnostic or prognosticresult, “f” is any mathematical function, “n” is any integer (e.g., aninteger from 1 to 10,000), and x1, x2, x3, x4, x5 . . . xn are the “n”parameters that are, for example, measurements determined by medicaldevices, clinical evaluation scores, and/or tests results for biologicalsamples (e.g., human biological samples such as blood, urine, orcerebrospinal fluid).

The parameters of an algorithm can be individually weighted. An exampleof such an algorithm is expressed in Formula 2:

Diagnostic score=a1*x1+a2*×2−a3*x3+a4*×4−a5*x5  (2)

Here, x1, x2, x3, x4, and x5 can be measurements determined by medicaldevices, clinical evaluation scores, and/or test results for biologicalsamples (e.g., human biological samples), and a1, a2, a3, a4, and a5 areweight-adjusted factors for x1, x2, x3, x4, and x5, respectively.

The diagnostic score can be used to quantitatively define a medicalcondition or disease, or the effect of a medical treatment. For example,an algorithm can be used to determine a diagnostic score for a disordersuch as depression. In such an embodiment, the degree of depression canbe defined based on Formula 1, with the following general formula:

Depression diagnosis score=f(x1,x2,x3,x4,x5 . . . xn)

The depression diagnosis score is a quantitative number that can be usedto measure the status or severity of depression in an individual, “f” isany mathematical function, “n” can be any integer (e.g., an integer from1 to 10,000), and x1, x2, x3, x4, x5 . . . xn are, for example, the “n”parameters that are measurements determined using medical devices,clinical evaluation scores, and/or test results for biological samples(e.g., human biological samples).

To determine what parameters are useful for inclusion in a diagnosticalgorithm, a biomarker library of analytes can be developed, andindividual analytes from the library can be evaluated for inclusion inan algorithm for a particular clinical condition. In the initial phasesof biomarker library development, the focus may be on broadly relevantclinical content, such as analytes indicative of inflammation, Th1 andTh2 immune responses, adhesion factors, and proteins involved in tissueremodeling (e.g., matrix metalloproteinases (MMPs) and tissue inhibitorsof matrix metalloproteinases (TIMP5)). In some embodiments (e.g., duringinitial library development), a library can include a dozen or moremarkers, a hundred markers, or several hundred markers. For example, abiomarker library can include a few hundred protein analytes. As abiomarker library is built, new markers can be added (e.g., markersspecific to individual disease states, and/or markers that are moregeneralized, such as growth factors). In some embodiments, analytes canbe added to expand the library and to increase specificity beyond theinflammation, oncology, and neuropsychological foci by addition ofdisease related proteins obtained from discovery research (e.g., usingdifferential display techniques, such as isotope coded affinity tags(ICAT) or mass spectroscopy).

The addition of a new analyte to a biomarker library can require apurified or recombinant molecule, as well as the appropriate antibody tocapture and detect the new analyte. It is noted that while applicationof a biomarker library to conventional ELISA platforms can requiremultiple antibodies for each analyte, use of the Precision HumanBiolaboratories, Inc. (PHB, Durham, N.C.) Molecular InteractionMeasurement System (MIMS) as described herein requires a single specificantibody for each analyte. Although discovery of individual “new ornovel” biomarkers is not necessary for developing useful algorithms,such markers can be included. The MIMS platform and other technologiesthat are suitable for multiple analyte detection methods as describedherein typically are flexible and open to addition of new analytes.

This document provides multiplexed detection systems that can providerobust and reliable measurement of analytes relevant to diagnosing,treating, and monitoring clinical conditions. The biomarker panels canbe expanded and transferred to label-free arrays, and algorithms can bedeveloped to support clinicians and clinical research.

Custom antibody array(s) can be designed, developed, and analyticallyvalidated for about 25-50 antigens. Initially, a panel of about 5 to 10(e.g., 5, 6, 7, 8, 9, or 10) analytes can be chosen based on theirability to, for example, distinguish affected from unaffected subjects,or to distinguish between stages of disease in patients from a definedsample set. An enriched database, however, usually one in which morethan 10 significant analytes are measured, can increase the sensitivityand specificity of test algorithms.

It is noted that such approaches also can be applied to other biologicalmolecules including, without limitation, DNA and RNA.

Selection of Individual Parameters

In the construction of libraries or panels, the markers and parameterscan be selected by any of a variety of methods. The primary driver forconstruction of a disease specific library or panel can be knowledge ofa parameter's relevance to the disease. To construct a library fordiabetes, for example, understanding of the disease would likely warrantthe inclusion of blood glucose levels. Literature searches orexperimentation also can be used to identify other parameters/markersfor inclusion. In the case of diabetes, for example, a literature searchmight indicate the potential usefulness of hemoglobin A1c (HbAC), whilespecific knowledge or experimentation might lead to inclusion of theinflammatory markers tumor necrosis factor (TNF)-α receptor 2,interleukin (IL)-6, and C-reactive protein (CRP), which have been shownto be elevated in subjects with type II diabetes.

FIG. 1 is a flow diagram detailing the first steps that can be includedin development of a disease specific library or panel for use indetermining, e.g., diagnosis or prognosis. The process can include twostatistical approaches: 1) testing the distribution of biomarkers forassociation with the disease by univariate analysis; and 2) clusteringthe biomarkers into groups using a tool that divides the biomarkers intonon-overlapping, uni-dimensional clusters, a process similar toprincipal component analysis. After the initial analysis, a subset oftwo or more biomarkers from each of the clusters can be identified todesign a panel for further analyses. The selection typically is based onthe statistical strength of the markers and current biologicalunderstanding of the disease.

FIG. 2 is a flow diagram depicting steps that can be included to developa disease specific library or panel for use in establishing diagnosis orprognosis, for example. As shown in FIG. 2, the selection of relevantbiomarkers need not be dependant upon the selection process described inFIG. 1, although the first process is efficient and can provide anexperimentally and statistically based selection of markers. The processcan be initiated, however, by a group of biomarkers selected entirely onthe basis of hypothesis and currently available data. The selection of arelevant patient population and appropriately matched (e.g., for age,sex, race, BMI, etc.) population of normal subjects typically isinvolved in the process. In some embodiments, patient diagnoses can bemade using state of the art methodology and, in some cases, by a singlegroup of physicians with relevant experience with the patientpopulation. Biomarker expression levels can be measured using the MIMSinstrument or any other suitable technology, including single assays(e.g., ELISA or PCR). Univariate and multivariate analyses can beperformed using conventional statistical tools (e.g., T-tests, principalcomponents analysis (PCA), linear discriminant analysis (LDA), or BinaryLogistic Regression).

FIG. 3 is a flow diagram depicting steps that can be included inestablishing a score for diagnostic development and application. Theprocess can involve obtaining a biological sample (e.g., a blood sample)from a subject to be tested. Depending upon the type of analysis beingperformed, serum, plasma, or blood cells can be isolated by standardtechniques. If the biological sample is to be tested immediately, thesample can be maintained at room temperature; otherwise the sample canbe refrigerated or frozen (e.g., at −80° C.) prior to assay. Biomarkerexpression levels can be measured using a MIMS instrument or any othersuitable technology, including single assays such as ELISA or PCR, forexample. Data for each marker are collected, and an algorithm is appliedto generate a diagnostic score. The diagnostic score, as well as theindividual analyte levels, can be provided to a clinician for use inestablishing a diagnosis for the subject.

Analyte Measurement

Any suitable method(s) can be used to quantify the parameters includedin a diagnostic/prognostic algorithm. For example, analyte measurementscan be obtained using one or more medical devices or clinical evaluationscores to assess a subject's condition, or using tests of biologicalsamples to determine the levels of particular analytes. As used herein,a “biological sample” is a sample that contains cells or cellularmaterial, from which nucleic acids, polypeptides, or other analytes canbe obtained. Useful biological samples include, without limitation,urine, blood, serum, plasma, cerebrospinal fluid, pleural fluid,bronchial lavages, sputum, peritoneal fluid, bladder washings,secretions (e.g., breast secretions), oral washings, swabs (e.g., oralswabs), tissue samples, touch preps, and fine-needle aspirates.

Measurements can be obtained separately for individual parameters, orcan be obtained simultaneously for a plurality of parameters. Anysuitable platform can be used to obtain measurements for parameters.Useful platforms for simultaneously quantifying multiple parametersinclude, for example, those described in U.S. Provisional ApplicationNos. 60/910,217 and 60/824,471, as well as PCT Publication No.WO2007/067819, all of which are incorporated herein by reference intheir entirety.

An example of a useful platform utilizes MIMS label-free assaytechnology, which has been developed by PHB. Briefly, local interferenceat the boundary of a thin film can be the basis for optical detectiontechnologies. For biomolecular interaction analysis, glass chips with aninterference layer of SiO₂ can be used as a sensor. Molecules binding atthe surface of this layer increase the optical thickness of theinterference film, which can be determined as set forth in U.S.Provisional Application Nos. 60/910,217 and 60/824,471, for example.

FIG. 4 illustrates the binding of an antigen to its specific antibody onan exemplary biochip described in the above referenced patent documents.This binding results in an increase in the optical thickness of theinterference film. As illustrated, this biochip includes having atransparent substrate and a transparent thin-film layer as aninterference layer. An optional reflective thin-film layer is depositedbetween the substrate and the transparent thin-film layer inimplementations when the refractive index of the substrate is similar tothe transparent thin-film layer. The biochip is illuminated by a probebeam of light. A first reflected light beam is reflected at theinterface between the substrate and the transparent thin-film layer orthe middle reflective thin-film layer. The input probe beam is alsoreflected as a second reflected light beam off the top surface of thetransparent thin-film layer. The two reflected beams are analyzed toobtain height information of the substrate and the transparent thin-filmlayer before immobilizing reagents on the reagent immobilizing sites.

The biochip in FIG. 4 can be designed to include multiple reagentimmobilizing sample sites arranged in an array on the surface of thetransparent thin-film layer for immobilizing reagent molecules. Thesubstrate can be designed to include one or more calibration structuresdeposited on the top surface to provide on-chip calibration. Inaddition, a set of alignment marks can be formed on the substrate toenable a scanner or imager to identify and locate a target spot (e.g., areagent immobilizing sample site). These alignment marks can beimplemented to encode location information such as the positioncoordinates (x,y) of the target spots. An position encoding scheme caninclude a bar code, or a simple number. In some implementations, thealignment markers can include markers with high image contrast for easydetection. The alignment markers can so be used as alignment visualmarkers to guide a x-y moving stage holding a biochip to setup andconfigure an initial scanning position. Such alignment markers canenhance accuracy of aligning different scan data together. Details ofthese and other features are described in U.S. Provisional ApplicationNos. 60/910,217 and 60/824,471 and PCT Publication No. WO2007/067819.

To measure molecular interactions, an array of different antibodies canbe immobilized on a sensor chip surface, and a hyperspectrum imagingsystem can be used to scan the chip to acquire interference-relatedspectrum information over the entire sensor chip surface. In oneimplementation, this hyperspectrum imaging system for detectingmolecular interaction includes a light probe having a light sourcedesigned to direct a probe beam of light along an optical light path,one or more optical lenses located along the light path, and an opticalgrating located along the light path. The optical grating is designed toswitch between a zero optical mode to turn off the dispersive functionof the grating and a first optical mode to diffract input light into thefirst diffraction order of the grating. The optical probe includes anoptical slit designed to switch between a position on the light path anda position off the light path. A sample stage is also included with theimaging system with the sample stage designed to hold and move a biochipalong a predetermined plane perpendicular to the light path. The imagingsystem further includes an image sensor located along the light pathwith the image sensor designed to capture the probe beam of lightreflected off the substrate. Implementations can optionally include oneor more of the following features. The imaging system can include animaging mode controller designed to switch the optical grating betweenthe zero optical mode and the first optical mode. The imaging modecontroller can also move the optical slit between the position on thelight path and the position off the light path. In some implementations,the imaging mode controller can also switch the optical grating to thefirst optical mode and move the optical slit on the light path in such amanner as to direct a single line of the reflected beam of light isthrough the optical slit and the optical grating to disperse the singleline of the reflected beam of light into spectral components.

In addition, implementations can optionally include one or more of thefollowing features. The imaging system can include one or moreadditional optical slits and optical gratings. Also, the imaging modecontroller can be designed to switch the optical gratings to the firstoptical mode and move the optical slits on the light path in such amanner as to direct a different line of the reflected beam of lightthrough each of the optical slits and the optical gratings to disperseeach line of the reflected beam of light into spectral components. imagesensor can be designed to determine the height at the reagentimmobilizing area based on the spectral components of the reflected beamof light. The light source can be designed to direct a probe beam oflight having a coherent length based on a height of the substrate.Further the image sensor can include a charge coupled device (CCD) imagesensor. Alternatively, the image sensor can include a complementarymetal oxide semiconductor (CMOS) image sensor.

FIGS. 5-7 provide examples of the data output from a MIMS instrument. Inparticular, FIG. 5 shows a two-dimensional view of anti-IL-1 betaprinted (about 0.15 pg) on the nanostructured chip (16 spots are shown).A three-dimensional view of the front portion of the same area is shownin FIG. 6. The nanobars are 20 nanometers high, and the volume of thepeak is proportional to the concentration of antibody printed on thesurface. FIG. 7 shows a three-dimensional view of the IL-1 beta antibody(2-fold concentrations) reacted with an IL-1 beta standard. The graph inthe right panel indicates the changes in peak height before (upperpeaks) and after (lower peaks) reaction with IL-1 beta.

Another example of platform useful for multiplexing is the FDA approved,flow-based Luminex assay system (xMAP; World Wide Web atluminexcorp.com). This multiplex technology uses flow cytometry todetect antibody/peptide/oligonucleotide or receptor tagged and labeledmicrospheres. Since the system is open in architecture, Luminex can bereadily adapted to host particular disease panels.

The invention will be further described in the following examples, whichdo not limit the scope of the invention described in the claims.

EXAMPLES Example 1 Diagnostic Markers of Depression

Methods provided herein were used to develop an algorithm fordetermining depression scores that are useful to, for example, diagnoseor determine predisposition to major depressive disorder (MDD), orevaluate response to anti-depressive therapeutics. The development ofpsychotropic drugs has relied on the quantification of disease severitythrough psychopathological parameters (e.g., the Hamilton scale fordepression). Subjective factors and lack of a proper definitioninevitably influence such parameters. Similarly, diagnostic parametersfor enrollment of psychiatric patients in phase II and phase IIIclinical studies are centered on the assessment of disease severity andspecificity by measurement of symptomatological scales, and there are novalidated biological correlates for disease trait and state that couldhelp in patient selection. In spite of recent progress in moleculardiagnostics, the potential information contained within the patientgenotype on the likely phenotypic response to drug treatment has notbeen effectively captured, particularly in non-research settings.

The immune system has a complex and dynamic relationship with thenervous system, both in health and disease. The immune system surveysthe central and peripheral nervous systems, and can be activated inresponse to foreign proteins, infectious agents, stress and neoplasia.Conversely, the nervous system modulates immune system function boththrough the neuroendocrine axis and through vagus nerve efferents. Whenthis dynamic relationship is perturbed, neuropsychiatric diseases canresult. In fact, several medical illnesses that are characterized bychronic inflammatory responses (e.g., rheumatoid arthritis) have beenreported to be accompanied by depression. In addition, administration ofproinflammatory cytokines (e.g., in cancer or hepatitis C therapies) caninduce depressive symptomatology. Administration of proinflammatorycytokines in animals induces “sickness behavior,” which is a pattern ofbehavioral alterations that is very similar to the behavioral symptomsof depression in humans. Thus, the “Inflammatory Response System (IRS)model of depression” (Maes (1999) Adv. Exp. Med. Biol. 461:25-46)proposes that proinflammatory cytokines, acting as neuromodulators,represent key factors in mediation of the behavioral, neuroendocrine andneurochemical features of depressive disorders.

Multiplexed detection systems such as those described herein were usedto phenotype molecular correlates of depression. Preliminary studiesindicated the value in using multiplexed antibody arrays to develop apanel of biomarkers in populations with MDD. The availability ofbiological markers reflecting psychiatric state (e.g., the type ofpathology, severity, likelihood of positive response to treatment, andvulnerability to relapse) will greatly impact both the appropriatediagnosis and treatment of depression.

The systematic, highly parallel, combinatorial approach to assemble“disease specific signatures” using algorithms as described herein isused to determine the status of or predisposition to MDD, and also isused to predict an individual's response to therapy. Toward that end, abiomarker library—a collection of tests useful to quantify proteinsexpressed in human serum—was developed.

Results

Panels were initially developed by analyzing results using twostatistical approaches: 1) testing the distribution of biomarkers for anassociation with disease by univariate analysis; and 2) clustering thebiomarkers into groups using a variable clustering (VARCLUS) tool thatdivides the biomarkers into non-overlapping uni-dimensional clusters, aprocess similar to principal component analysis. Each cluster'spredictive value was determined by computing individual clustercentroid's partial regression coefficient with the affected group, usingpartial least squares discriminant analysis (PLS-DA). After the initialanalysis, the panel was made functionally redundant, and a subset of 2-4biomarkers from each of the 8 clusters with the highest statisticalsignificance was identified. The final selection was based on thestatistical strength of the markers and current biological understandingof the disease.

Preliminary studies included patients meeting the criteria for recurrentmajor depressive disorder according to the DSM IV scale. The studies ofdepression described herein were conducted on a population of AshkenaziJewish (AJ) males, which was chosen for the studies due to the relativegenetic homogeneity of the population and to limit gender-relateddifferences. In addition, the frequency of several genes responsible for“single-gene” disorders and disease predisposition is higher amongAshkenazi Jews than among Sephardic Jews and non-Jews. All patientsstudied were from a single medical center to avoid site-relateddifferences in the diagnosis of depression. To provide a sufficientnumber of subjects (110 total, including 55 controls and 55 depressedindividuals), males between the ages of 18 and 70 years were included.

Multivariate analysis using the Partial Least Squares based softwareSIMCA-P (Umetrics, Umea, Sweden) readily segregated the 55 controls fromthe 55 depressed members of the AJ male cohort. FIG. 8 shows the resultsof multivariate analysis of sera from control and MDD patients, whichwere analyzed using a biomarker library panel of about 85 serum proteinanalytes. Similar results were obtained for a subset of subjects rangingfrom 30 to 49 years of age. The library of 85 analytes that was used forthe initial study was biased toward inflammatory markers. The libraryalso included chemokines, enzymes involved in tissue remodeling (e.g.,MMPs and TIMPs), and several hormones (e.g., human growth hormone anderythropoietin, which had been shown to be elevated in the CSF ofdepressed patients).

Two statistical approaches were then used for biomarker assessment andalgorithm development: (1) univariate analysis for testing thedistribution of biomarkers for association with MDD; and (2) lineardiscriminant analysis (LDA) and binary logistic regression for algorithmconstruction.

Univariate analysis of individual analyte levels: Using the Students Ttest, serum levels of each of the analytes tested using Luminexmultiplex technology were analyzed for comparison of depressed versusnormal subjects. The level of significance was set at α≦0.05. Theresults of this analysis for sixteen significant analytes are shown inTable 1. Results of the analysis for a different combination of sevenbiomarkers is shown in Table 2. The potential relationship of eachanalyte to depression is indicated in Table 3.

TABLE 1 Mean serum levels and p values for 16 MDD biomarkers Mean SerumMean Serum Analyte Level MDD Level Control p value IL-13 17.42 pg/ml40.35 pg/ml 1.20E−06 IL-7 10.52 pg/ml 22.92 pg/ml 2.80E−05 GST 8.13ng/ml 25.62 ng/ml 0.0005 IL-18 333.7 pg/ml 267 pg/ml 0.0018 A2M 0.996mg/ml 0.776 mg/ml 0.0011 IL-15 0.955 pg/ml 1.51 pg/ml 0.0005 IL-10 3.4pg/ml 5.55 pg/ml 0.0007 Factor VII 373.4 ng/ml 455.1 ng/ml 0.0007 EGF56.08 pg/ml 90.30 pg/ml 0.01 FABP 2.15 ng/ml 1.57 ng/ml 0.004 PAI-176.58 pg/ml 89.66 pg/ml 0.01 BDNF 20.12 ng/ml 17.36 ng/ml 0.012 RANTES41.36 pg/ml 34.49 pg/ml 0.0049 TIMP-1 173.3 ng/ml 194.2 ng/ml 0.038Alpha 1 Antitrypsin 1.77 mg/ml 1.65 mg/ml 0.0085 Beta 2 Microglobulin1.87 mg/ml 2.17 mg/ml 0.0125

TABLE 2 Mean serum levels and p values for 7 MDD Biomarkers Serum LevelSerum Level Normal MDD Analyte Mean Mean p value IL-10 5.84 pg/ml 2.304pg/ml 0.008 IL-13 48 pg/ml 13.73 pg/ml 0.0002 IL-18 268 pg/ml 424.2pg/ml 0.0038 Cortisol 269 ng/ml 390 ng/ml 0.0028 A2M 0.959 mg/ml 1.313mg/ml 0.009 Thyroxine 6.23 ng/ml 5.35 ng/ml 0.016 BDNF 23.09 ng/ml 17.76ng/ml 0.029

TABLE 3 Analyte Relationship to Depression IL-13 IL-13 usually acts asan anti-inflammatory cytokine IL-7 IL-7 may be a neuronal growth factorGST stress related; tricyclics reduce level IL-18 stress related releaseof IL-18 in CNS and plasma A2M acute phase protein associated withinflammatory disease IL-15 IL-15 is a novel proinflammatory cytokineIL-10 IL-10 usually acts as an anti-inflammatory cytokine Factor VII oneof the central proteins in the coagulation cascade. EGF growth factorinvolved in neuroplasticity & the EGF- R TK cascade FABP FABPs controlintracellular transport and storage of lipids PAI-1 tPA/plasminogensystem may play a role in MDD pathogenesis BDNF neuroplasticity, lowerin MDD, responds to treatment RANTES RANTES may serve to amplifyinflammatory responses in CNS TIMP-1 extracellular matrix remodeling inphysiological & pathological processes A1AT reduced activity ofpeptidases can occur in MDD B2M can be associated with chronicinflammatory conditions Cortisol stress hormone that can be elevated inMDD Thyroxine (T₄) serum T₄ is important for the action of thyroidhormones in the brain

As examples of the data presented in Tables 1 and 2, FIGS. 9 and 10 showthe serum levels of BDNF and IL-13, respectively, in MDD patients and innormal subjects. The mean level of BDNF in the MDD group (17.76±5.27)was lower than in the normal subject group (23.06±5.45). Whileunivariate analysis identified BDNF as a marker with statisticalsignificance (p=0.029), the ranges of BDNF levels for the two groupsoverlap significantly. Thus, serum BDNF by itself is not a goodpredictor of MDD. Similarly, univariate analysis identified IL-13 as themarker with highest statistical significance (p=0.0002), suggesting thatit could be a class predictor. As shown in FIG. 10, however, there wassignificant overlap in the range of serum IL-13 levels between MDDsubjects and controls. In fact, no single analyte exhibited a clearseparation with non-overlapping levels for the patient-control subjectcomparisons. Therefore, single analytes cannot be used as a classpredictor for MDD.

PCA and PLS-DA: PCA is mathematically defined as an orthogonal lineartransformation that transforms the data to a new coordinate system suchthat the greatest variance by any projection of the data comes to lie onthe first coordinate (called the first principal component), the secondgreatest variance on the second coordinate, and so on. PCA can be usedfor dimensionality reduction in a data set by retaining thosecharacteristics of the data set that contribute most to its variance, bykeeping lower-order principal components and ignoring higher-order ones.Such low-order components often contain the “most important” aspects ofthe data.

PLS-DA was performed in order to sharpen the separation between groupsof observations, by rotating PCA components such that a maximumseparation among classes is obtained, and to understand which variablescarry the class separating information. PLS-DA and other techniques wereused to demonstrate the segregation of normal subjects and depressedpatients using the MDD panel to measure serum levels of 16 analytes,five analytes, four analytes, and three analytes as examples.

An Algorithm Based Upon Linear Discriminant Analysis (LDA): In order toidentify the analytes that contribute the most to discrimination betweenclasses (e.g., depressed vs. normal), a stepwise method of LDA from SPSS11.0 for Windows was used with following settings: Wilks' lambda (Λ)method was used to select analytes that maximize the cluster separationand analyte entrance into the model was controlled by its F-value. Alarge F-value indicates that the level of the particular analyte isdifferent between the two groups, and a small F-value (F<1) indicatesthat there is no difference. In this method, the null hypothesis isrejected for small values of Λ. Thus, the aim was to minimize Λ.

To construct a list of analyte predictors, the F-values for each of theanalytes was calculated. Starting with the analyte having the largestF-value (the analyte that differs the most between the two groups), thevalue of Λ was determined. The analyte with the next largest F-value wasthen added to the list and Λ was recalculated. If the addition of thesecond analyte lowered the value of Λ, it was kept in the list ofanalyte predictors. The process of adding analytes one at a time wasrepeated until the reduction of Λ no longer occurred.

Cross-validation, a method for testing the robustness of a predictionmodel, was then carried out. To cross-validate a prediction model, onesample is removed and set aside, the remaining samples are used to builda prediction model based on the pre-selected analyte predictors, and adetermination is made as to whether the new model is able to predict theone sample not used in building the new model correctly. This process isrepeated for all samples one at a time, and a cumulativecross-validation rate then can be calculated. The final list of analytepredictors was determined by manually entering and removing analytes tomaximize the cross-validation rate, using information obtained from theunivariate analyses and cross-validations. The final analyte classifieris then defined as the set of analyte predictors that gives the highestcross-validation rate.

Examples of data on the individual markers are discussed below, and aredepicted in FIGS. 9-26. In most of the figures, data from individualsubjects is shown as a dot, and the median and 5^(th) and 95^(th)percentiles are indicated by lines. This is adjacent in each case tolines indicating the mean and standard deviation for each group.

BDNF: BDNF has been suggested to play a role in depression. BDNF levelsare reduced in depressed patients as compared to controls, andantidepressant treatment has been shown to increase serum BDNF levels indepressed patients. The level of plasma BDNF also can be increased withelectroconvulsive therapy, suggesting that non-drug therapy can modulateBDNF levels (Marano et al. (2007) J. Clin. Psych. 68:512-7). Asdescribed above and shown in FIG. 9, the mean level of BDNF in the MDDgroup (17.76±5.27 ng/ml) was lower than the normal subject group(23.06±5.45), with a p value of 0.029. While univariate analysisidentified BDNF as a marker with statistical significance, however, theranges of BDNF levels for the two groups overlap significantly,indicating that serum BDNF by itself is not a good predictor of MDD.

Interleukin 18: Psychological and physical stresses have been reportedto exacerbate auto-immune and inflammatory diseases. Plasmaconcentrations of IL-18 have been shown to be significantly elevated inpatients with major depression disorder or panic disorder as comparedwith normal controls. The elevation of plasma IL-18 levels may reflectincreased production and release of IL-18 in the central nervous systemunder stressful settings (see, e.g., Sekiyama (2005) Immunity22:669-77). As shown in FIG. 11, the inventors confirmed that IL-18levels are higher in MDD patients than in normal controls (p=0.0038).Although evaluating IL-18 provided some differentiation of depressedpatients from control subjects, this single marker test does not havesufficient diagnostic discrimination power or the robustness to be usedin clinical practice.

Fatty Acid Binding Protein: The brain is highly enriched in long-chainpolyunsaturated fatty acids (PUFAs), which play important roles in brainstructural and biologic functions. Plasma transport, in the form of freefatty acids or esterified FAs in lysophosphatidylcholine andlipoproteins, and de-novo synthesis contribute to brain accretion oflong-chain PUFAs. docosahexaenoic acid (DHA) is an antidepressant(Mischoulon and Fava (2000) Psychiatr. Clin. North Am. 23:785-94), andFABP has been shown to be elevated in stroke and neurodegenerativediseases (Pelsers and Glatz (2005) Clin. Chem. Lab. Med. 43:802-809; andZimmermann-Ivol et al. (2004) Mol. Cell. Proteomics 3:66-72.) As shownin FIG. 12, FABP was elevated in MDD patients as compared to controls(p=0.0056).

Interleukin 10: Depression is associated with activation of theinflammatory response system. Evidence suggests that pro-inflammatoryand anti-inflammatory cytokine imbalance affects the pathophysiology ofmajor depression. Pro-inflammatory cytokines are mainly mediated byT-helper (Th)-1 cells, and include IL-1β, IL-6, TNF-α, and interferon-γ.Anti-inflammatory cytokines are mediated by Th-2 cells, and includeIL-4, IL-5, and IL-10. In humans, antidepressants significantly increaseproduction of IL-10. As shown in FIG. 13, IL-10 levels were lower inplasma of MDD subjects as compared to controls (p=0.008).

Interleukin 7: Like IL-10, levels of IL-7 in plasma also were in reducedin depressed male subjects as compared to controls. IL-7 is ahematopoietic cytokine with critical functions in both B- andT-lymphocyte development. IL-7 also exhibits trophic properties in thedeveloping brain. The direct neurotrophic properties of IL-7 combinedwith the expression of ligand and receptor in developing brain suggestthat IL-7 may be a neuronal growth factor of physiological significanceduring central nervous system ontogeny (Michealson et al. (1996) Dev.Biol. 179:251-263). Adult neurogenesis has been implicated in theetiology and treatment of depression. Elevated stress hormone levels,which are present in some depressed patients and can precipitate theonset of depression, reduce neurogenesis in animal models. Conversely,virtually all antidepressant treatments, including drugs of variousclasses, electroconvulsive therapy, and behavioral treatments, increaseneurogenesis (Drew and Hen (2007) CNS Neurol. Disord. Drug Targets6:205-218). As shown in FIG. 14, IL-7 levels were reduced in MMDpatients as compared to normal controls (p=2.8e⁻⁵).

Glutathione S-Transferase: Tricyclic antidepressants are known toinhibit the activity of GST pi isolated from different regions of humanbrain (e.g., the parietal cortex, frontal cortex, and brain stem). Theinhibitory effect depends more on chemical structure than on brainlocalization of the enzyme. Tricyclics bind nonspecifically to theeffector site of GST. The inhibitory effect of tricyclic antidepressantson brain GST may decrease the efficiency of the enzymatic barrier thatprotects the brain against toxic electrophiles, and may contribute intheir adverse effects. On the other hand, brain GST may decrease thetherapeutic effects of tricyclic antidepressants by binding them asligands (Baranczyk-Kuzma et al. (2001) Pol. Merkur Lekarski 11:472-475.)As depicted in FIG. 15, mean levels of GST in the plasma of MDD subjectswas reduced as compared to normal controls (p=0.00047).

EGF: Among the different factors that may be involved inneuroplasticity, glial cells use growth factor members of the EGFfamily, acting via receptors endowed with tyrosine kinase activity, toproduce morphological changes and release neuroactive substances thatdirectly excite nearby neurons. Agonists of tyrosine-kinase receptors(e.g., NGF, EGF, and basic FGF) enhance Na⁺-dependent serotonin uptakein the synaptosomal-enriched P(2) fraction from rat-brain (Gil et al.(2003) Neurochem. Int. 42:535-542). As shown in FIG. 16, serum levels ofEGF were decreased in MDD as compared to normal controls (p=0.01).

IL-13: IL-13 typically acts as an anti-inflammatory cytokine, suggestingthat a lower level of IL-13 might increase the dysregulation of theimmune system, resulting in increased proinflammatory cytokine activity.Systemic administration of the bacterial endotoxin lipopolysaccharide(LPS) has profound depressive effects on behavior that are mediated byinducible expression of proinflammatory cytokines such as IL-1, IL-6,and tumor necrosis factor-alpha (TNF-alpha) in the brain. When both LPSand IL-13 were co-injected, IL-13 potentiated the depressive effect(Bluthe et al. (2001) Neuroreport 12:3979-3983). As shown in FIG. 10,IL-13 levels were lower in depressed subjects than in normal controls(p=1.2e⁻⁶).

PAI-1: Tissue-type plasminogen activator (tPA) is a highly specificserine proteinase that catalyses the generation of zymogen plasminogenfrom the proteinase plasmin. Proteolytic cleavage of proBDNF, a BDNFprecursor, to BDNF by plasmin represents a mechanism by which BDNFaction is controlled. Furthermore, studies using mice deficient in tPAhas demonstrated that tPA is important for the stress reaction, a commonprecipitating factor for MDD. Serum levels of the PAI-1, the majorinhibitor of tPA, have been shown to be higher in women with MDD than innormal controls. See, e.g., Tsai (2006) Med. Hypotheses 66:319-322). Asshown in FIG. 17, however, the inventors found that PAI-1 levels werelower in the serum of depressed male subjects as compared to normalcontrols (p=0.01).

RANTES: Regulated upon Activation, Normal T-cell Expressed, and Secreted(RANTES; also known as CCL5) is an 8 kDa protein classified as achemotactic cytokine or chemokine RANTES is chemotactic for T cells,eosinophils and basophils, and plays an active role in recruitingleukocytes into inflammatory sites. The combined effects of RANTES mayserve to amplify inflammatory responses within the central nervoussystem (Luo et al. (2002) Glia 39:19-30). As shown in FIG. 18, serumlevels of RANTES were elevated in subjects with MDD as compared tonormal controls (p=0.007).

Factor VII: Psychological stressors and depressive and anxiety disordersalso are associated with coronary artery disease. Changes in bloodcoagulation, anticoagulant, and fibrinolytic activity may constitutepsychobiological pathways that link psychological factors with coronarysyndromes (von Kanel et al. (2001) Psychosom. Med. 63:531-544). As shownin FIG. 19, levels of Factor VII were found to be lower in subjects withMDD as compared to normal controls (p=0.0007). This finding is contraryto some reports of hypercoagulation in depressed patients, particularlythose with cardiovascular problems. However, depression has been shownto be associated with inflammation and coagulation factors incardiovascular disease-free people, suggesting a possible pathway thatleads to an increased frequency of events of coronary heart disease indepressive individuals (Panagiotakos (2004) Eur. Heart J. 25:492-499).

IL-15: IL-15 is a proinflammatory cytokine that is involved in thepathogenesis of inflammatory/autoimmune disease. In addition, IL-15 hasbeen shown to be somatogenic (Kubota et al. (2001) Am. J. Physiol.Regul. Integr. Comp. Physiol. 281:R1004—R1012). As shown in FIG. 20,IL-15 levels were lower in depressed subjects than in normal controls(p=0.0005).

TIMP-1: Matrix metalloproteinases (MMPs) and the tissue inhibitors ofmetalloproteinases (TIMP5), whose expression can be controlled bycytokines, play a role in extracellular matrix remodeling inphysiological and pathological processes. A positive association betweenplasma norepinephrine levels and MMP-2 protein levels, as well as anegative correlation between plasma cortisol levels and MMP-2 levels,has been observed (Yang et al. (2002) J. Neuroimmunol. 133:144-150). Asshown in FIG. 21, TIMP-1 levels were significantly lower in depressedsubjects than in normal controls (p=0.038).

Alpha-1 antitrypsin: Reduced activity of peptidases, such asprolylendopeptidase (PEP) and dipeptidyl peptidase IV (DPP IV), occursin depression. As shown in FIG. 22, alpha-1 antitrypsin levels werelower in male subjects with depression than in normal controls(p=0.0085). This finding was in contrast to studies indicating thatincreased plasma concentrations of alpha-1 antitrypsin are found inseverely depressed subjects as compared with healthy controls, withminor depressives exhibiting an intermediate position (Maes (1992) J.Affect. Disord. 24:183-192). It is possible that the MDD populationsused in the present studies included significantly more moderatedepressives.

A2M: A2M is a serum pan-protease inhibitor and an acute phase proteinthat has been associated with inflammatory disease. A2M also has beenimplicated in Alzheimer disease based on its ability to mediate theclearance and degradation of A beta, the major component of beta-amyloiddeposits. Non-melancholic depressive patients have showed increased A2Mserum concentrations in the acute stage of disease and after 2 and 4weeks of treatment (Kirchner (2001) J. Affect. Disord. 63:93-102).Consistent with this finding, MDD patients were found to have increasedlevels of serum A2M than normal controls (p=0.0024; FIG. 23). In thepresent studies, there was no attempt to segregate melancholic fromnon-meloncholic patients.

Beta 2 Microglobulin (B2M): B2M is a small (99 amino acid) protein thatplays a key role in immunological defense. B2M can be modified byremoval of the lysine at position 58, leaving the protein with twodisulfide-linked chains of the amino acids 1-57 and 59-99. This modifiedform (desLys-58-β2-microglobulin, or ΔK58-β2m) has been shown to beassociated with chronic inflammatory conditions (Nissen (1993) DanishMed. Bul. 40:56-64). B2M has been found to correlate with diseaseactivity in several autoimmune disorders, and is used as apharmacodynamic marker of interferon beta treatment in multiplesclerosis.

As shown in FIG. 24, B2M levels were elevated in serum from MDD patientsas compared to normal controls (p=0.013).

Cortisol: Cortisol is a corticosteroid hormone produced by the adrenalcortex of the adrenal gland. Cortisol is a vital hormone that is oftenreferred to as the “stress hormone,” as it is involved in the responseto stress. This hormone increases blood pressure and blood sugar levels,and has an immunosuppressive action. Cortisol inhibits secretion ofcorticotropin-releasing hormone (CRH), resulting in feedback inhibitionof ACTH secretion. This normal feedback system may break down whenhumans are exposed to chronic stress, and may be an underlying cause ofdepression. Hypercortisolism in depression has been reported, asreflected by elevated mean 24-hour serum cortisol concentrations andincreased 24-hour urinary excretion of cortisol. In addition, prolongedhypercortisolemia may be neurotoxic, and recurrent depression episodesassociated with elevated cortisol may lead to progressive brain damage.As shown in FIG. 25, cortisol levels in normal subjects (269±46.7 pg/ml)were significantly lower than in subjects with MDD (390±100.4 pg/ml;p=0.0028).

Thyroxine (T₄); T₄ is involved in controlling the rate of metabolicprocesses in the body and influencing physical development. The thyroidgland and thyroid hormones generally are believed to be important in thepathogenesis of major depression. For example, studies have documentedalterations in components of the hypothalamic-pituitary-thyroid (HPT)axis in patients with primary depression. Screening thyroid tests,however, often add little to diagnostic evaluation, and overt thyroiddisease is rare among depressed inpatients. The finding that depressioncan co-exist with autoimmune subclinical thyroiditis suggests thatdepression may cause alterations in the immune system, or that it couldbe an autoimmune disorder itself. The outcome of treatment and thecourse of depression may be related to thyroid status as well.Augmentation of antidepressant therapy with co-administration of thyroidhormones (mainly T₃) is a treatment option for refractory depressedpatients. As shown in FIG. 26, thyroxine levels in normal subjects(6.23±0.842 ng/ml) were higher than in subjects with MDD (5.35±0.622ng/ml; p=0.016).

Using 15 of the markers listed above (IL-7, IL-10, IL-13, IL-15, IL-18,BDNF, FABP, GST, EGF, RANTES, TIMP-1, A1AT, PAI-1, factor VII, and T₄),as well as ACTH, a diagnostic score was established based on thefollowing algorithm:

Depression diagnosisscore=f(a1*analyte1+a2*analyte2+a3*analyte3+a4*analyte4+a5*analyte5+a6*analyte6+a7*analyte7+a8*analyte8+a9*analyte9+a10*analyte10+a11*analyte11+a12*analyte12+a13*analyte13+a14*analyte14+a15*analyte15+a16*analyte16).

More specifically, a depression score was assigned to each subject usingPLS-DA. The depression diagnostic score for each subject was calculated,and the results are plotted in FIG. 27. The scores were determinedstatistically to have a sensitivity >90% and a specificity>85%.

Using five of the markers listed above (A2M, BDNF, IL-10, IL-13, andIL-18), a diagnostic score was established based on the followingalgorithm:

Depression diagnosisscore=f(a1*analyte1+a2*analyte2+a3*analyte3+a4*analyte4+a5*analyte5).

Here, a1, a2, a3, a4, and a5 are the B values shown in Table 4. Thealgorithm had a sensitivity of 86.3% and a specificity of 77.1%.

Several examples of depression algorithms using different marker setswere established and are shown in Tables 5-10. In particular, MDDalgorithms with subsets of 4 analytes are shown in Table 5 (A2M, BDNF,IL-10, and IL-13), Table 6 (A2M, BDNF, IL 10, and IL-18), and Table 7(A2M, BDNF, IL-13, and IL-18). Algorithms with subsets of 3 analytes areshown in Table 8 (A2M, BDNF, and IL-10), Table 9 (A2M, BDNF, and IL-13),and Table 10 (A2M, BDNF, and IL-18).

TABLE 4 Algorithm with A2M, BDNF, IL-10, IL-13, and IL-18 B S.E. Wald dfSig. Exp(B) A2M .748 .867 .745 1 .388 2.114 BDNF −.231 0.67 11.984 1.001 .794 IL-10 −.457 .151 9.208 1 .002 .633 IL-13 −.029 .020 2.265 1.132 .971 IL-18 0.011 .003 10.570 1 .001 1.011 Constant 2.822 1.8002.456 1 .117 16.806 Predicted Sick1 Percentage Observed 0 1 correctSICK1 0 37 11 77.1 1 7 44 86.3 Overall percentage 81.8

TABLE 5 Algorithm with A2M, BDNF, IL-10, and IL-13 B S.E. Wald df Sig.Exp(B) A2M .739 .787 .883 1 .347 2.095 BDNF −.161 0.55 8.551 1 .003 .851IL-10 −.206 .110 3.521 1 .061 .814 IL-13 −.048 .017 8.025 1 .005 .954Constant 4.254 1.625 6.854 1 .009 70.405 Predicted Sick1 PercentageObserved 0 1 correct SICK1 0 32 16 66.7 1 11 40 78.4 Overall percentage72.7

TABLE 6 Algorithm with A2M, BDNF, IL-10, and IL-18 B S.E. Wald df Sig.Exp(B) A2M .922 .871 1.122 1 .290 2.515 BDNF −.228 .065 12.152 1 .000.796 IL-10 −.553 .137 16.275 1 .000 .575 IL-18 0.13 .003 13.999 1 .0001.013 Constant 1.973 1.695 1.355 1 .244 7.190 Predicted Sick1 PercentageObserved 0 1 correct SICK1 0 36 12 75.0 1 10 41 80.4 Overall percentage77.8

TABLE 7 Algorithm with A2M, BDNF, IL-13, and IL-18 B S.E. Wald df Sig.Exp(B) A2M 1.148 .824 1.941 1 .164 3.151 BDNF −.165 .057 8.336 1 .004.848 IL-13 −.058 .016 12.308 1 .000 .944 IL-18 .007 .003 5.894 1 .0151.007 Constant 1.486 1.637 .824 1 .364 4.419 Predicted Sick1 PercentageObserved 0 1 correct SICK1 0 33 15 68.8 1 10 42 80.8 Overall percentage75.0

TABLE 8 Algorithm with A2M, BDNF, and IL-10 B S.E. Wald df Sig. Exp(B)A2M 1.209 .755 2.563 1 .109 3.351 BDNF −.139 .049 7.986 1 .005 .781IL-10 −.345 .102 11.318 1 .001 .708 Constant 2.994 1.403 4.550 1 .03319.956 Predicted Sick1 Percentage Observed 0 1 correct SICK1 0 36 1275.0 1 13 38 74.5 Overall percentage 74.7

TABLE 9 Algorithm with A2M, BDNF, and IL-13 B S.E. Wald df Sig. Exp(B)A2M 1.009 .781 1.670 1 .196 2.744 BDNF −.137 .052 7.019 1 .008 .872IL-13 −.059 .016 14.652 1 .000 .942 Constant 3.083 1.444 4.560 1 .03321.832 Predicted Sick1 Percentage Observed 0 1 correct SICK1 0 32 1666.7 1 11 41 78.8 Overall percentage 73.0

TABLE 10 Algorithm with A2M, BDNF, and IL-18 B S.E. Wald df Sig. Exp(B)A2M 2.111 .763 7.652 1 .006 8.261 BDNF −.117 .047 6.319 1 .012 .889IL-18 .007 .003 8.205 1 .004 1.007 Constant −1.691 1.258 1.807 1 .179.184 Predicted Sick1 Percentage Observed 0 1 correct SICK1 0 35 13 72.91 12 40 76.9 Overall percentage 75.0

Further Studies

A number of questions have been raised about serum markers forneuropsychiatric diseases. For example, previous studies investigatingtestosterone levels and mood disorders showed conflicting results. Inthe present studies, however, no significant differences were notedbetween levels of testosterone in 12 depressed males as compared toage-matched normal controls.

Similarly, in a study of depressed psychiatric inpatients and normalcontrols, platelet serotonin (blood serotonin) content was significantlyhigher among depressed psychiatric inpatients with a recent case of amood disorder than among depressed psychiatric inpatients without recenthistory of mood disorder. Other results suggested that depressedpatients with abnormal personality disorder had higher levels ofplatelet serotonin than patients without personality disorder. Inaddition to similarities between 5-HT2A serotonin receptors in plateletsand brain, levels of serotonin transporter (SERT) in platelet membranesare identical to those found in the CNS. A number of studies have showna reduction in SERT density in platelets of depressed individualscompared to SERT density in platelets of healthy subjects.

More often than not, results from a single assay, or a group of assaysconsidered as single assays rather than an algorithm, do not providevaluable information about a single patient. For example, a number ofstudies have indicated that BDNF is lower in depressed patients ascompared to controls. Indeed, several studies have demonstrated thatantidepressants increase BDNF levels, and that activating theBDNF-signaling pathway may play an important role in their therapeuticmechanism. FIG. 9 shows the levels of serum BDNF in the AJ malepopulation (p=0.029 for depressed vs. controls). One of the criticalaspects of any sample set is the documentation available. Since manysamples are taken for reasons other than plasma protein phenotyping, thedata relevant to the time of day and the last time the patient took anymedication is often missing. It should be pointed out, however, thatwhile there was variation in when the individual blood samples analyzedherein were drawn, perhaps leading to increased patient variation, thesamples were taken during normal working hours so that marked changesdue to diurnal variation would not be expected.

An expanded library of antibodies (PHB's highly multiplexed screeningtechnology, with a capacity of about 200 markers) is extended to samples(e.g., plasma or serum) from well-characterized patients. Antibodies forproteins of interest (e.g., monoamines and thyroid hormones) areevaluated. Markers associated with neuropsychiatric disease also areevaluated (e.g., in collaboration with academic laboratories doing massspectroscopy-based discovery in CSF from depressed subjects).

Since the initial studies were limited with regard to antidepressantnaive subjects, patients who are antidepressant naïve are identified forthe extension studies. Such subjects are imaged with magnetic resonancespectroscopy, including phosphorus magnetic resonance spectroscopy(³¹P-MRS) studies. Studies have suggested that cerebral metabolicchanges are implicated in the pathology of MDD. Experiments using³¹P-MRS have shown that cerebral energy metabolism [e.g.,beta-nucleoside triphosphate (beta-NTP), primarily reflecting brainlevels of adenosine triphosphate (ATP)] is lower in depressed subjectsthan in normal controls, and is positively correlated with severity ofdepression. Beta-NTP levels also appear to correct after successfulantidepressant treatment, but not in treatment of non-responders. The³¹P-MRS methods described herein include 3D chemical shift imaging andthe possibility to measure ³¹P-MRS metabolites from specific brainregions. Inclusion-exclusion criteria, study design, randomization andtreatment, as well as specific instruments to be used for clinicalcharacterization of subjects are determined using standard protocols.

The sensitivity and specificity of custom protein arrays fordetermination of multiple biomarkers from blood, sera, cerebrospinalfluid, and/or urine are determined. In addition, appropriate algorithmsare developed that reflect concordance between protein signatures andimaging, as well as psychological testing.

The antibody arrays are designed to provide a fast, reliable,high-throughput, sensitive, and quantitative detection tool for multipledifferentially expressed antigens (including annotated proteins andpost-translationally modified proteins, for example) from a limitedamount of sample (e.g., 20 to 50 μl of serum). Surfaces and arraydesigns are developed to be compatible with samples obtained through aminimally invasive method in order to provide the opportunity forsequential sampling. Sera or plasma typically are used, but, asindicated herein, other biological samples also may be useful. Forexample, specific monoamines can be measured in urine. In addition,depressed patients as a group have been found to excrete greater amountsof catecholamines (CAs) and metabolites in urine than healthy controlsubjects. Analytes of interest include, for example, norepinephrine,epinephrine, vanillylmandelic acid (VMA), and3-methoxy-4-hydroxyphenylglycol (MHPG). Proteomic studies have indicatedthat urine is a rich source of proteins and peptides that may bedifferentially expressed in disease states.

While the initial study focused on males, the findings are extended towomen, since after puberty women are twice as likely as men to developdepression. Since estrogen may be associated with some of theage-related changes in mood (e.g., post-partum depression, or depressionin the perimenopausal period) estrogen levels are concomitantly measuredin studies of female subjects.

Example 2 Diagnostic Markers of Cancer

An algorithm is developed to determine diagnostic/prognostic scores forcancer (e.g., breast cancer). Breast cancer is the most common femalecancer in the United States, the second most common cause of cancerdeath in women (after lung cancer), and the main cause of death in womenages 45 to 55. Every year, approximately 205,000 American women arediagnosed with breast cancer, and more than 40,000 die from it. Earlydetection is a major factor contributing to the 3.2% annual decline inbreast cancer death rates over the past 5 years. Unfortunately,currently available breast cancer screening tools such as mammographyand breast examination miss up to 40% of early breast cancers and areleast effective in detecting cancer in young women, whose tumors areoften more aggressive.

Breast cancers are classified histologically based upon the types andpatterns of cells from which they are composed. Carcinomas can beinvasive (extending into the surrounding stroma) or non-invasive(confined just to the ducts or lobules). Histologic features and breastcancer grade have been correlated with expression of receptors such asHer2/neu, estrogen receptor (ER), and progesterone receptor (PR). Forexample, overexpression of HER2, p53, vascular endothelial growth factor(VEGF), and cyclin E proteins in primary tumors has been shown topredict increased risk for metastatic disease and decreased survival,while expression of ER and PR are associated with favorable outcomes.While these and other antigens (e.g., Ki67, p53, and bcl-2) maycorrelate with tumor grade, they do not appear to be independentprognostic factors. Other factors have been found to play a role in thecomplex progression of ductal carcinoma in situ (DCIS), includingextracellular matrix regulation and degradation, cell cycle regulation,angiogenesis, and mitogenesis factors. Comprehensive analysis is notalways possible, given the complexity of the disease, the number ofpotential markers, the invasiveness of the procedure, restricted accessto sequential samples and limited tissue obtained by needle biopsy, andthe potential cost. Nonetheless, recent developments in gene andproteomic profiling of pre-cancerous and cancerous lesions suggest thatpatterns of markers may be useful as a holistic phenotype of anindividual's disease and response.

To date, only ER and PR status are accepted predictors forresponsiveness, and are used in clinical decision making for adjuvantendocrine treatment. There is a dearth of soluble biomarkers that can bedetected from serum both pre- and post-operatively. Given the importanceof early diagnosis, staging and monitoring therapy, the development ofnoninvasive techniques to aid in detection of cancer and determinationof prognosis is of crucial importance.

Serum or other body fluid markers in addition to ER and PR are utilizedfor diagnosis and monitoring. Access to the markers utilized in themethods described herein is obtained through, for example, interactionswith academic, government, and industry laboratories. It is noted,however, that the platform remains flexible and open to the addition ofnew analytes. Potential additional markers include the following.

ErbB-2: Human-epidermal-growth-factor receptor 2 (ErbB-2, p185HER2) is atransmembrane glycoprotein with an intracellular tyrosine kinaseactivity and an extracellular domain similar to those of the EGF-bindingdomain of the EGF receptor. The use of ErbB2 as a marker for response totherapy is controversial, although the extracellular domain (ECD) of theErbB-2 protein is frequently cleaved and released into the circulation,where it can be detected by ELISA in about 45% of patients withmetastatic breast cancer. Increased serum HER2/neu concentrations havebeen associated with progressive metastatic disease, as well as and poorresponse to chemotherapy and hormonal therapy. The inventors havedeveloped an assay for ErbB-2, and have utilized the antibody pairs in acustom SearchLight™ multiplex.

CA-15-3: The CA 15-3 assay has had limited use in the management ofstage 11 and III breast cancer patients. Patients with confirmed breastcarcinoma frequently have CA 15-3 assay values in the same range ashealthy individuals, and elevated levels can occur in subjects withoutmalignant breast carcinoma. CA 15-3 thus may not be useful formonitoring, unless the marker is high at the time of initial treatment.Nonetheless, studies have indicated the utility of this marker forserial monitoring of patients. CA 15-3 levels can be used in conjunctionwith other clinical methods for monitoring breast cancer, and may bemore useful in the context of a multiplex. Antibodies and assay kits areavailable for CA-15-3 detection in blood, and the assay can be adaptedto the platforms utilized herein.

Mammaglobin: Mammaglobin is a 10 kDa glycoprotein that is distantlyrelated to a family of epithelial secretory proteins such as the humanClara cell 10 kDa protein (CC10)/uteroglobin. Indeed, mammaglobin, amammary-specific member of the uterglobin family, is overexpressed inhuman breast cancer and is detectable in sera of breast cancer patients.

Angiogenesis and proteins involved in tissue remodeling: Breastcarcinoma, like most other solid tumors, needs to develop an angiogenicphenotype for invasiveness, progression and metastasis. VEGF levels insera appear to be useful for staging patients, and therefore may bevaluable for patient monitoring. In earlier studies, a SearchLight™panel was developed that determined the levels of MMPs and TIMPs insera.

Autoantibodies: It is known that sera from breast and other cancerscontain antibodies that react with tumor-associated antigens. Althoughthese antibodies occur relatively infrequently and have low affinity lowaffinity for known tumor associated antigens, the label-free MIMStechnology is particularly suitable to phenotyping an antibody responseto a specific antigen (see, e.g., Notkins (2007) Sci. Amer. 71:72-79).The inventors have had modest success in printing both protein andpeptide antigens on nanostructured chips and capturing specificantibody. A major advantage of the MIMS system is that a large number ofantigens can be printed and rapidly optically scanned on the chip in theabsence of secondary antibody. Both the pattern of autoantibodiesexpressed as well as quantitative changes in the amount of antibodydetected over time (e.g., subsequent to radiation) can be measured byMIMS. In addition, since autoantibodies may appear in nipple aspiratefluid (NAF), it may be useful to measure autoantibodies in NAF for earlydetection or monitoring recurrence.

Example 3 Diagnostic Markers of Chronic Obstructive Pulmonary Disease(COPD)

COPD is a disease state characterized by airflow limitation that is notfully reversible. The clinical hallmark of COPD is an accelerateddecline in lung function with aging. In most cases the airflowlimitation represents, in part, an abnormal inflammatory response in theairways to noxious particles and gases (Pauwels et al. (2001) Am. J.Respir. Crit. Care Med. 163:1256-1276). COPD is recognized as a majorglobal health issue that affects over 5% of the adult population, and isthe only major cause of death in industrialized nations in whichmorbidity and mortality are increasing. Over the next twenty years,mortality due to COPD is expected to increase five-fold. The true healthburden of COPD is underestimated, however, because airflow obstructionis an important contributor to other common causes of morbidity andmortality, including ischemic heart disease, stroke, pneumonia and lungcancer (Engstrom et al. (2001) Circulation 103:3086-3091; Sin and Mann(2005) Proc. Am. Thorac. Soc. 2:8-11; Mannino et al. (2003) Arch.Intern. Med. 163:1475-1480; and Hole et al. (1996) Brit. Med. J.313:711-715).

COPD is a systemic disorder, the extrapulmonary manifestations of whichinvolve diverse organs and include skeletal muscle dysfunction, musclewasting, osteoporosis, and atherosclerosis and its associatedcomplications. Weight loss in patients with COPD may be related toincreased circulating levels of inflammatory mediators (e.g., tumornecrosis factor alpha and inflammatory cytokines). Importantly, there isa general association between the severity of the airflow obstructionand the severity of extrapulmonary end-organ damage in patients withCOPD (Andreassen and Vestbo (2003) Eur. Respir. J. Suppl. 22:2s-4-s).The potential effects of COPD on the cardiovascular system are importantclinically because data from large longitudinal studies of COPD patientsindicate that the leading cause of hospitalization and mortality inestablished COPD patients is cardiovascular in nature (Anthonisen et al.(2002) Am. J. Respir. Crit. Care Med. 166:333-339; and Camilli et al.(1991) Am. J. Epidemiol. 133:795-800). Poor lung function has been shownto be as important a predictor of cardiac mortality as well establishedrisk factors such as total serum cholesterol. How COPD increases therisk of poor cardiovascular outcomes is largely unknown, however. Apotential mechanism is that pulmonary inflammation leads to systemicinflammation.

There is uncertainty and debate about the best way to screen for anddiagnose COPD. Airflow restriction in COPD usually is progressive andassociated with an abnormal inflammatory response in the lung. Airflowlimitation is the slowing of expiratory airflow as measured byspirometry, with a consistently low forced expiratory volume in onesecond (FEV1) that can be reversed to some extent with inhaledcorticosteroids or bronchodialators.

Although the degree of FEV1 impairment seen after bronchodilatorinhalation is a fairly good prognostic marker in the study of COPDpopulations, the extent of FEV1 decreases do not correlate well withreduced quality of life in individual patients. A dyspnea index or amore complex index such as the BODE (including measures of dyspnea,exercise capacity, and systemic dysfunction such as weight loss (Celliet al. (2004) N. Engl. J. Med. 350:1005-1012)) correlates better withquality of life impairment. Changes in FEV1 occur slowly over the courseof COPD, and such changes can be difficult to apply to clinical studiesof new therapeutics.

Acute-on-chronic deteriorations of respiratory health in COPD are termedexacerbations. Exacerbations contribute to decline in lung function(Donaldson et al. (2002) Thorax 57:847-852), impairment to health status(Seemungal et al. (1998) Am. J. Respir. Crit. Care Med. 157:1418-1425),hospital admissions (Ashton et al. (1995) Q. J. Med. 88:661-672), andtherefore healthcare costs and mortality (Soler-Cantalufia et al. (2005)Thorax 60:925-931). Patients who experience more than 2 exacerbationsper year are especially difficult to manage. Several potential host,pathogen, and treatment factors have been identified that contribute torecurrent exacerbation. There is a substantial need, however, tounderstand the etiology and identify efficacious interventions to reducethe frequency of COPD exacerbations.

Results

Though there is debate as to whether the systemic manifestations of COPDare solely the result of the lung disease or if COPD is a systemicdisease that mainly manifests in the lungs, changes in serum proteinlevels may be useful in diagnosis and treatment (Pinto-Plata et al.(2007) Thorax 62:595-601). In these studies, the final panel includedmultiple analytes that had a statistically significant correlation withFEV1, diffusing capacity of the lung for carbon monoxide (DLCO), sixminute walk distance (6MWD), BODE scale, and exacerbation frequency, butno correlation with body mass index (BMI; Pinto-Plata et al. (2007)Thorax 62:595-601). From these studies, it was concluded that serumbiomarkers can be useful in the diagnosis of COPD, and may be useful infuture treatment strategies.

Further Studies

Based on earlier studies, a panel of analytes is developed into an arrayor arrays that provide an objective aid to identification and monitoringof patients with COPD, with emphasis on patient stratification andidentification of frequent exacerbators. Custom antibody array(s) forabout 25 to 50 antigens are designed, developed, and analyticallyvalidated in sera from well characterized patients from a single site.Algorithm(s) are then developed that reflect concordance between proteinsignatures and BODE score, in addition to spirometry. The algorithms areevaluated using investigator blinded test sets. This panel is initiallydeveloped on a Luminex or Pierce SearchLight™ technology platform, andthen is transferred to PHB nanostructured chips.

The aforementioned panel of systemic biomarkers (Pinto-Plata et al.(2007) Thorax 62:595-601) was not useful in predicting exacerbationseverity. Nonetheless, the acute-phase response at exacerbation was moststrongly related to indices of monocyte function. Patient samplesobtained pre- and post-exacerbation were heterogenous with regard to thecausative agent, exacerbation frequency, and severity (Hurst et al.(2006) Am. J. Respir. Crit. Care Med. 174:867-874). Samples for furtherstudies are better characterized with regard to the causative agent, andCOPD severity is assessed by BODE score as well as spirometry.

Given the failure of the panel to predict exacerbation frequency, whichis critical to patient stratification, additional analytes are evaluatedfor a patient stratification panel. These include the following.

Monocyte/macrophage and neutrophil activation markers markers: A studyof smokers and “never-smokers” concluded that “healthy” smoking men withnear normal FEV1 show signs of inflammation in the lower airways thatare related to a decrease in DLCO and to emphysematous lesions on highresolution CT. As compared to never-smokers, smokers had higher bloodlevels of myeloperoxidase (MPO), human neutrophil lipocalin (HNL),eosinophil cationic protein (ECP), and lysozyme, higher levels of MPO,interleukin-8 (IL-8), and HNL in bronchial lavage (BL), and higherlevels of IL-8, HNL, and interleukin-1α (IL-1α) in bronchoalveolarlavage (BAL). Smokers also had lower levels of Clara cell protein 16(CC-16, an abundant component of airway secretions) in blood. HNL in BLand BAL showed strong correlation to other inflammatory markers,including MPO, IL-8, and IL-1α. The observed inflammation appeared to bethe result of both monocyte/macrophage and neutrophil activation. Infurther studies, MIP-1α and HCC1 are used as potential markers.

Bacterial and viral antigens/antibodies: Studies in COPD patients havedemonstrated that significant changes in antibody titers to putativepathogens can occur in the absence of symptoms, i.e., during periods ofdisease quiescence (Smith et al. (1976) Lancet 1:1253-1255) and duringexacerbations even when sputum cultures were negative for the bacteriumbeing studied (Reichek et al. (1970) Am. Rev. Respir. Dis. 101:238-244).Viral antigens and antibodies (e.g., RSV (Wilkinson et. al. (2006) Am.J. Respir. Crit. Care Med. 173:871-876) also can be detected in theblood of patients prior to and during exacerbations. It is possible thatplasma profiling will provide some rapid insight into treatment ofexacerbations.

Once an antibody array is validated, an algorithm is designed forapplication of multiple biomarkers from sera, plasma, bronchiolarlavage, or sputum to diagnosis, prognosis, and patient stratificationfor clinical trials. Similarly, such panels are used for monitoring thetreatment of COPD patients by identification of pharmacodynamic markers.

Example 4 A Method for Evaluating the Potential Side-Effect Profile of aChemical Entity

A chemical entity (compound) is immobilized on a surface directly orindirectly. A biological sample from a human or other animal is added tothe surface to measure the interaction of the chemical entity (compound)with any particular biological molecules, including but not limited topolypeptides, DNA, RNA, lipids, and carbohydrates. The above biologicalsamples are prepared from any and all organs, tissues, or biologicalfluids of human or other animals in which a side effect to the chemicalentity is to be evaluated. In particular, the above surface on which thechemical entity is immobilized is part of a label-free system such asPHB's MIMS, which is used to detect interactions of the chemical entitywith biological molecules. FIG. 28 depicts the scheme of such aniterative evaluation process.

A potential side-effect of the chemical entity are identified based oninteractions of the chemical entity with biological molecules. Afterbinding to the chemical entity, molecules can be eluted (e.g., with highsalt, urea, or detergent) and further characterized using massspectrometry or any other appropriate biochemical tests. By applyingthis method sequentially to a series of different tissue and bodyfluids, a potential toxicity profile is generated for a compound veryearly in the drug development process. This procedure can result indevelopment of highly specific and valuable information. For example, ifthere is strong binding of a chemical compound to a protein from a heartmuscle sample using the above process, it is an indication that thischemical entity may have an effect on the heart. If elution andpurification of proteins bound to the chemical entity reveals thepresence of, for example, myosin binding protein C (MyBP-C, one of themajor sarcomeric proteins involved in the pathophysiology of familialhypertrophic cardiomyopathy), then insight is gained into the potentialmechanism.

Other Embodiments

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Otheraspects, advantages, and modifications are within the scope of thefollowing claims.

1. A process for diagnosing a human subject as having or not havingdepression, comprising: (a) obtaining a measured level for each of alpha1 antitrypsin (A1AT), cortisol, and brain-derived neurotrophic factor(BDNF) in a blood or urine sample from the subject; (b) comparing themeasured levels with control levels of A1AT, cortisol, and BDNF in bloodor urine samples, respectively, from normal human subjects who do nothave depression; and (c) diagnosing the subject as having depression if(i) the measured levels of A1AT and BDNF are lower than the controllevels for A1AT and BDNF and (ii) the measured level of cortisol ishigher than the control level for cortisol, or diagnosing the subject asnot having depression if (i) the measured levels of A1AT and BDNF arenot lower than the control levels for A1AT and BDNF and (ii) themeasured level of cortisol is not higher than the control level forcortisol.
 2. The process of claim 1, wherein said depression is a majordepression disorder.
 3. The process of claim 1, wherein said bloodsample is whole blood.
 4. The process of claim 1, wherein said bloodsample is serum.
 5. The process of claim 1, wherein said blood sample isplasma.
 6. A process for diagnosing a human subject as havingdepression, comprising: (a) obtaining a measured level for each of alpha1 antitrypsin (A1AT), cortisol, and brain-derived neurotrophic factor(BDNF) in a blood or urine sample from the subject; (b) comparing themeasured levels with control levels of A1AT, cortisol, and BDNF in bloodor urine samples, respectively, from normal human subjects who do nothave depression; and (c) diagnosing the subject as having depression if(i) the measured levels of A1AT and BDNF are lower than the controllevels for A1AT and BDNF and (ii) the measured level of cortisol ishigher than the control level for cortisol.
 7. The process of claim 6,wherein said depression is a major depression disorder.
 8. The processof claim 6, wherein said blood sample is whole blood.
 9. The process ofclaim 6, wherein said blood sample is serum.
 10. The process of claim 6,wherein said blood sample is plasma.
 11. A process for diagnosing ahuman subject as not having depression, comprising: (a) obtaining ameasured level for each of alpha 1 antitrypsin (A1AT), cortisol, andbrain-derived neurotrophic factor (BDNF) in a blood or urine sample fromthe subject; (b) comparing the measured levels with control levels ofA1AT, cortisol, and BDNF in blood or urine samples, respectively, fromnormal human subjects who do not have depression; and (c) diagnosing thesubject as not having depression if (i) the measured levels of A1AT andBDNF are not lower than the control levels for A1AT and BDNF and (ii)the measured level of cortisol is not higher than the control level forcortisol.
 12. The process of claim 11, wherein said depression is amajor depression disorder.
 13. The process of claim 11, wherein saidblood sample is whole blood.
 14. The process of claim 11, wherein saidblood sample is serum.
 15. The process of claim 11, wherein said bloodsample is plasma.